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Background The clinicopathological features and prognosis of breast cancer in Asia are different from those in the Western countries. Tumor‐infiltrating immune cells can influence the outcome of patients with breast cancer, but they have not been systemically evaluated in Asian patients with breast cancer. Methods We compared the immune score, composition, and prognostic impact of infiltrating immune cells between Asian and Western patients with breast cancer by analyzing gene expression profiles from eight Gene Expression Omnibus data sets and The Cancer Genome Atlas data set. The Estimation of Stromal and Immune Cells in Malignant Tumours Using Expression Data (ESTIMATE) and Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT) algorithms were used to determine the immune score and composition of tumor‐infiltrating immune cells, respectively. Findings This study included 462 Asian patients and 2,186 Western patients. Tumors of Asian patients had significantly higher immune score, particularly in the luminal B and HER2‐enriched subtypes. High immune score was associated with favorable prognosis in both Asian and Western patients, and Asian race with a high ESTIMATE immune score provided additional power to predict longer disease‐free survival. Activated CD4 T cells and M2 macrophages were the most strongly associated with survival in both Asian and Western patients. Interpretation Our study highlights the difference in tumor immune microenvironments between Asian and Western patients. The higher ESTIMATE immune score, which represents more abundant tumor‐infiltrating immune cells, in tumors of Asian patients partly explains their favorable prognosis. Implications for Practice The tumor microenvironment serves as an interface that affects the human body's reaction to cancer cells. Evidence has revealed that tumor‐infiltrating immune cells were associated with patient prognosis. This study demonstrated the disparity of tumor microenvironments and their prognostic impact between Asian and Western patients with breast cancer. The differences in immune score partially explained the racial survival differences noted in recent studies. Integrated analysis of tumor cells, tumor microenvironment, and racial effect may significantly improve recurrence risk prediction for patients with stage I–III breast cancer. Because the effect of tumor microenvironment varies across different populations, a model of interaction between immune score and race/ethnicity is recommended in accessing the risk of patients with cancer.

Background: Tumor-infiltrating leukocytes (TILs) are immune cells surrounding tumor cells, and several studies have shown that TILs are potential survival predictors in different cancers. However, few studies have dissected the differences between hepatitis B- and hepatitis C-related hepatocellular carcinoma (HBV−HCC and HCV−HCC). Therefore, we aimed to determine whether the abundance and composition of TILs are potential predictors for survival outcomes in HCC and which TILs are the most significant predictors. Methods: Two bioinformatics algorithms, ESTIMATE and CIBERSORT, were utilized to analyze the gene expression profiles from 6 datasets, from which the abundance of corresponding TILs was inferred. The ESTIMATE algorithm examined the overall abundance of TILs, whereas the CIBERSORT algorithm reported the relative abundance of 22 different TILs. Both HBV−HCC and HCV−HCC were analyzed. Results: The results indicated that the total abundance of TILs was higher in non-tumor tissue regardless of the HCC type. Alternatively, the specific TILs associated with overall survival (OS) and recurrence-free survival (RFS) varied between subtypes. For example, in HBV−HCC, plasma cells (hazard ratio [HR] = 1.05; 95% CI 1.00–1.10; p = 0.034) and activated dendritic cells (HR = 1.08; 95% CI 1.01–1.17; p = 0.03) were significantly associated with OS, whereas in HCV−HCC, monocytes (HR = 1.21) were significantly associated with OS. Furthermore, for RFS, CD8+ T cells (HR = 0.98) and M0 macrophages (HR = 1.02) were potential biomarkers in HBV−HCC, whereas neutrophils (HR = 1.01) were an independent predictor in HCV−HCC. Lastly, in both HBV−HCC and HCV−HCC, CD8+ T cells (HR = 0.97) and activated dendritic cells (HR = 1.09) had a significant association with OS, while γ delta T cells (HR = 1.04), monocytes (HR = 1.05), M0 macrophages (HR = 1.04), M1 macrophages (HR = 1.02), and activated dendritic cells (HR = 1.15) were highly associated with RFS. Conclusions: These findings demonstrated that TILs are potential survival predictors in HCC and different kinds of TILs are observed according to the virus type. Therefore, further investigations are warranted to elucidate the role of TILs in HCC, which may improve immunotherapy outcomes.

Liver kinase B1 (LKB1) is a tumor suppressor, and its loss might lead to activation of the mammalian target of rapamycin (mTOR) and tumorigenesis. This study aimed to determine the clinical relevance of LKB1 gene and protein expression in breast cancer patients. LKB1 protein expression was evaluated using immunohistochemistry in tumors from early breast cancer patients in two Taiwanese medical centers. Data on LKB1 gene expression were obtained from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data set. The correlations between LKB1 expression, clinicopathologic factors, and patient outcome were analyzed. LKB1 expression was significantly associated with estrogen receptor (ER) expression in 2 of the 4 cohorts, but not with other clinicopathologic factors. LKB1 expression was not a predictor for relapse-free survival, overall survival (OS), or breast cancer-specific survival. In a subgroup analysis of the two Taiwanese cohorts, high LKB1 protein expression was predictive of high OS in human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients (P = 0.013). Our study results indicate that LKB1 expression is not prognostic in the whole population of breast cancer patients, but it is a potential predictor of OS in the subset of HER2-positive patients

Identified genetic variants from genome wide association studies frequently show only modest effects on the disease risk, leading to the "missing heritability" problem. An avenue, to account for a part of this "missingness" is to evaluate gene-gene interactions (epistasis) thereby elucidating their effect on complex diseases. This can potentially help with identifying gene functions, pathways, and drug targets. However, the exhaustive evaluation of all possible genetic interactions among millions of single nucleotide polymorphisms (SNPs) raises several issues, otherwise known as the "curse of dimensionality". The dimensionality involved

Hypertension has been reported as a major risk factor for diseases such as cardiovascular disease, and associations between platelet activation and risk for hypertension are well-established. However, the exact nature of causality between them remains unclear. In this study, a bi-directional Mendelian randomization (MR) analysis was conducted on 15,996 healthy Taiwanese individuals aged between 30 and 70 years from the Taiwan Biobank, recorded between 2008 and 2015. The inverse variance weighted (IVW) method was applied to determine the causal relationship between platelet count and hypertension with single nucleotide polymorphisms as instrumental variables (IVs). Furthermore, to check for pleiotropy and validity of the IVs, sensitivity analyses were performed using the MR-Egger, weighted median and simple median methods. This study provided evidence in support of a positive causal effect of platelet count on the risk of hypertension (odds ratio: 1.149, 95% confidence interval: 1.131–1.578, P < 0.05), using the weighted median method. A significant causal effect of platelet count on hypertension was observed using the IVW method. No pleiotropy was observed. The causal effect of hypertension on platelet count was found to be non-significant. Therefore, the findings from this study provide evidence that higher platelet count may have a significant causal effect on the elevated risk of hypertension for the general population of Taiwan.

In East Asia, the breast cancer incidence rate among women aged <50 years has rapidly increased. Emerging tumors are distinctly characterized by a high prevalence of estrogen receptor (ER)–positive/human epidermal growth factor receptor (HER2)–negative cancer. In the present study, we identified unique genetic alterations in these emerging tumors. We analyzed gene copy number variations (CNVs) in breast tumors from 120 Taiwanese patients, and obtained public datasets of CNV and gene expression (GE). The data regarding CNV and GE were separately compared between East Asian and Western patients, and the overlapping genes identified in the comparisons were explored to identify the gene–gene interaction networks. In the age <50 years/ER + /HER2– subgroup, tumors of East Asian patients exhibited a higher frequency of copy number loss in APOA1/C3/A4/A5, a lipid-metabolizing gene cluster (33 vs. 10%, P < .001) and lower APOA1/C3/A4/A5 expressions than tumors of Western patients. These copy number loss related– and GE–related results were validated in another Taiwanese cohort and in two GE datasets, respectively. The copy number loss was significantly associated with poor survival among Western patients, but not among East Asian patients. Lower APOA1, APOC3, and APOA5 expressions were associated with higher ESTIMATE immune scores, indicating an abundance of tumor-infiltrating immune cells. In conclusion, APOA1/C3/A4/A5 copy number loss was more prevalent in luminal breast tumors among East Asian women aged <50 years, and its immunomodulatory effect on the tumor microenvironment possibly plays various roles in the tumor biology of East Asian patients.

Motivation Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference. Results We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual’s multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis. Availability and Implementation R function and instruction are available from the authors’ website: https://www4.stat.ncsu.edu/∼jytzeng/Software/TR.omics/TRinstruction.pdf Supplementary information Supplementary materials are available at Bioinformatics online.